Non Destructive Defect Detection by Spectral Density Analysis

The potential nondestructive diagnostics of solid objects is discussed in this article. The whole process is accomplished by consecutive steps involving software analysis of the vibration power spectrum (eventually acoustic emissions) created during the normal operation of the diagnosed device or un...

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Main Authors: Robert Frischer, Ondrej Krejcar
Format: Article
Language:English
Published: MDPI AG 2011-02-01
Series:Sensors
Subjects:
FFT
Online Access:http://www.mdpi.com/1424-8220/11/3/2334/
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spelling doaj-e9359c7113664f2ab3db49b27cc032372020-11-24T22:16:23ZengMDPI AGSensors1424-82202011-02-011132334234610.3390/s110302334Non Destructive Defect Detection by Spectral Density AnalysisRobert FrischerOndrej KrejcarThe potential nondestructive diagnostics of solid objects is discussed in this article. The whole process is accomplished by consecutive steps involving software analysis of the vibration power spectrum (eventually acoustic emissions) created during the normal operation of the diagnosed device or under unexpected situations. Another option is to create an artificial pulse, which can help us to determine the actual state of the diagnosed device. The main idea of this method is based on the analysis of the current power spectrum density of the received signal and its postprocessing in the Matlab environment with a following sample comparison in the Statistica software environment. The last step, which is comparison of samples, is the most important, because it is possible to determine the status of the examined object at a given time. Nowadays samples are compared only visually, but this method can’t produce good results. Further the presented filter can choose relevant data from a huge group of data, which originate from applying FFT (Fast Fourier Transform). On the other hand, using this approach they can be subjected to analysis with the assistance of a neural network. If correct and high-quality starting data are provided to the initial network, we are able to analyze other samples and state in which condition a certain object is. The success rate of this approximation, based on our testing of the solution, is now 85.7%. With further improvement of the filter, it could be even greater. Finally it is possible to detect defective conditions or upcoming limiting states of examined objects/materials by using only one device which contains HW and SW parts. This kind of detection can provide significant financial savings in certain cases (such as continuous casting of iron where it could save hundreds of thousands of USD). http://www.mdpi.com/1424-8220/11/3/2334/FFTpower spectrumMatLabStatisticadefect
collection DOAJ
language English
format Article
sources DOAJ
author Robert Frischer
Ondrej Krejcar
spellingShingle Robert Frischer
Ondrej Krejcar
Non Destructive Defect Detection by Spectral Density Analysis
Sensors
FFT
power spectrum
MatLab
Statistica
defect
author_facet Robert Frischer
Ondrej Krejcar
author_sort Robert Frischer
title Non Destructive Defect Detection by Spectral Density Analysis
title_short Non Destructive Defect Detection by Spectral Density Analysis
title_full Non Destructive Defect Detection by Spectral Density Analysis
title_fullStr Non Destructive Defect Detection by Spectral Density Analysis
title_full_unstemmed Non Destructive Defect Detection by Spectral Density Analysis
title_sort non destructive defect detection by spectral density analysis
publisher MDPI AG
series Sensors
issn 1424-8220
publishDate 2011-02-01
description The potential nondestructive diagnostics of solid objects is discussed in this article. The whole process is accomplished by consecutive steps involving software analysis of the vibration power spectrum (eventually acoustic emissions) created during the normal operation of the diagnosed device or under unexpected situations. Another option is to create an artificial pulse, which can help us to determine the actual state of the diagnosed device. The main idea of this method is based on the analysis of the current power spectrum density of the received signal and its postprocessing in the Matlab environment with a following sample comparison in the Statistica software environment. The last step, which is comparison of samples, is the most important, because it is possible to determine the status of the examined object at a given time. Nowadays samples are compared only visually, but this method can’t produce good results. Further the presented filter can choose relevant data from a huge group of data, which originate from applying FFT (Fast Fourier Transform). On the other hand, using this approach they can be subjected to analysis with the assistance of a neural network. If correct and high-quality starting data are provided to the initial network, we are able to analyze other samples and state in which condition a certain object is. The success rate of this approximation, based on our testing of the solution, is now 85.7%. With further improvement of the filter, it could be even greater. Finally it is possible to detect defective conditions or upcoming limiting states of examined objects/materials by using only one device which contains HW and SW parts. This kind of detection can provide significant financial savings in certain cases (such as continuous casting of iron where it could save hundreds of thousands of USD).
topic FFT
power spectrum
MatLab
Statistica
defect
url http://www.mdpi.com/1424-8220/11/3/2334/
work_keys_str_mv AT robertfrischer nondestructivedefectdetectionbyspectraldensityanalysis
AT ondrejkrejcar nondestructivedefectdetectionbyspectraldensityanalysis
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